Method and System for Researching Sales Effects of Advertising Using Association Analysis

ABSTRACT

A method and system for data mining is disclosed in which advertising/promotional events (e.g., the fact that a promotion was taking place when the transaction was recorded) are factored into association analysis of the data. Sales data is integrated with advertising data and a plurality of taxonomies are employed to link the merchandise data with the advertising data. This enhances the data so that the advertising status (advertised or non-advertised) can be determined and this information can be used to track the impact of advertising on product dynamics in market baskets and on sales. Further, for those articles that are advertised, detailed information about the advertising used is also available and integrated into the analysis so that, for example, the effectiveness or lack of effectiveness of a particular advertisement can be determined.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of U.S. application Ser. No. 09/510,416, filedFeb. 22, 2000, the entire contents of which is hereby incorporated byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and system of data mining and,more particularly, to a method and system of data mining which usesassociation analysis to draw conclusions regarding the effect ofadvertising and sales promotions on product dynamics in market baskets.

2. Description of the Related Art

Data mining is a well known technology used to discover patterns andrelationships in data. Data mining involves the application of advancedstatistical analysis and modeling techniques to the data to find usefulpatterns and relationships. The resulting patterns and relationships areused in many applications in business to guide business actions and tomake predictions helpful in planning future business actions.

One of the types of data mining is called “association analysis,” oftenreferred to as “market basket analysis.” Association analysis revealspatterns in the form of “association rules” or “affinities.” Anassociation rule between products A and B can be expressed symbolicallyas A→B which translates to the statement: “Whenever product A is in amarket basket, then product B tends to be in the market basket as well.”This is an example of “product dynamics,” i.e., the effect of thepurchase of one product on another product.

In the folklore of data mining, one of the most repeated storiesillustrating product dynamics is that of the alleged discovery that beerand diapers frequently appear together in a shopping basket. Theexplanation given in this tale is that when fathers are sent out on anerrand to buy diapers, they often purchase a six pack of their favoritebeer as a reward. Using the association rule discussed above, thisexample would be expressed as “diapers→beer” or, translated, wheneverdiapers appear in a shopping basket, beer also tends to appear in thatshopping basket.

There are a number of measures that have historically been used tocharacterize the importance of a particular association rule. In thecontext of market basket analysis, these measures are calculated inrelation to all market baskets under consideration. The “confidence” ofa rule “A→B” is the probability that if a basket contains A it will alsocontain B. The “support” of a rule is the frequency of occurrence of therule in the set of all transactions. The “lift” of the rule is a measureof the predictive power of the premise A. Lift is a multiplier for theprobability of B in the presence of A versus the probability of Bwithout any prior knowledge of other items in the market basket.

For purposes of explanation, consider the following example: Table 1illustrates ten typical transactions representing the market baskets fora given day at a small store. From the data in the table, it can be seenthat diapers and beer appear together in some market baskets and we canconclude that when a transaction contains diapers, there is a tendencyfor it to also contain beer. Diapers appear in six transactions (1, 3,4, 8, 9, and 10) and beer appears in conjunction with diapers in four ofthese transactions (1, 3, 9, and 10). Therefore, the rule “diapers→beer”has a confidence of 4/6=67%. Further, there are four of the tentransactions where beer and diapers appear together. This results in avalue of 4/10=40% for the support of the rule. Finally, beer appears infive of the ten transactions while it appears in four of the sixtransactions containing diapers. This means that if a basket wasrandomly chosen without any prior information about any of thetransactions, there is a 5/10=50% chance of finding beer. However, if weuse the prior knowledge that if the basket contains diapers it has agood likelihood of also having beer, then the prospect of finding beeris improved if we choose from only baskets known to contain diapers,i.e., there is a 4/6=67% chance of finding beer. Thus, the lift of therule “diapers→beer” is 67%/50%=1.34.

TABLE 1 TRANSACTION MARKET BASKET 1 Diapers, beer, chips, soap 2 Chips,soap 3 Diapers, beer, soap 4 Diapers, chips, soap 5 Soap 6 Chips 7 Beer,chips 8 Diapers 9 Diapers, beer, soap 10 Diapers, beer, chips, soap

Association analysis techniques discover all association rules thatexceed set support and confidence thresholds. They also discover allsets of items that tend to occur in the same basket with a frequencythat exceeds the support threshold; such sets are termed “frequentitemsets.”

In recognition of the importance of data mining, tools have beendeveloped to perform the various data mining and modeling techniques.One such tool is Intelligent Miner™ sold by IBM. Intelligent Miner hasan outstanding algorithm for association analysis as part of its toolsuite. Being general purpose tools, Intelligent Miner and other datamining tools for association analysis reach the point of inferringfrequent itemsets and rules with their corresponding metrics ofinterest, such as support, confidence, and lift, but go no further.

Association rules express facts deduced from data. They are truestatements about the relationships observed in the data. These rules,along with their measures of confidence, support, and lift, can andshould be used to generate theories or hypotheses about the effects offuture actions that change the conditions under which the originalobservations were made. These hypotheses need to be posed in the complexand dynamic retail environment where potentially thousands of stores andtens of thousands of items must be considered against the backdrop ofpricing actions, promotions, campaigns, seasonality, and productavailability. Furthermore, all actions and results should be measuredagainst a matrix of revenue and profit rather than the abstract notionsof support, confidence, and lift.

Existing tools for association analysis do not factor in informationabout advertising and promotion and thus do not assist in developingtheories or hypotheses about their effects on product sales and productdynamics in market baskets. Moreover, association analysis as commonlyemployed focuses on product dynamics and does not analyze the aggregateproperties of individual baskets, referred to herein as “market basketdynamics.” If such analysis were conducted, it would yield data whichwould allow an understanding of overall buying behavior, measured at thelevel of market baskets, and what drives the overall buying behavior.Currently implemented association rules and frequent itemsets do notassist in determining information about the overall buying habits of theowner of a particular type of market basket; for example, what kinds ofproducts would be found in a “high-gross margin” baskets or whichproducts may drive such “high-gross margin” baskets.

Analysis of market basket data using data mining techniques, such asassociation analysis, is a recent development. Traditional methods forevaluating the effects of advertising and promotions on sales for aparticular item of interest focus on aggregate financial measures. Forexample, traditional approaches would measure the overall value ofshopping baskets that include or do not include the item of interest andcompute how these measures change as a function of promotion-relatedactions. These methods do not consider the overall content of shoppingbaskets (i.e., they focus only on items of interest) and thus do notexplain what these baskets tend to contain, nor do they reveal datawhich allows analysis of market basket dynamics. Without informationabout the relationships between the sale of various items and theirpromotion status it is not possible to explain any observed changes inthe aggregate measures. Moreover, by lumping together all baskets thatcontain the item of interest to compute an aggregate value, thesemethods do not allow for the possibility of having various types ofbaskets all containing the item of interest but with different dynamicsand thus different aggregate values.

Accordingly, a need exists for a method and system for utilizing datamining techniques to understand buying behavior which factors inadvertising and promotional actions on purchasing behavior.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a method and system fordata mining in which the premise and/or conclusion of an associationrule can include promotional or advertising events (e.g., the fact thata promotion was taking place when the transaction was recorded). Salesdata is integrated with advertising data and a plurality of taxonomiesare employed to link the merchandise data with the advertising data.This enhances the data so that the advertising status (advertised ornon-advertised) can be determined and this information can be used totrack the impact of advertising on product dynamics in market baskets.Further, for those articles that are advertised, detailed informationabout the advertising used is also available and integrated into theanalysis so that, for example, the effectiveness or lack ofeffectiveness of a particular advertisement, or elements thereof, can bedetermined.

Other objects and advantages of the present invention will be set forthin part in the description and the drawings which follow, and, in part,will be obvious from the description or may be learned by practice ofthe invention.

To achieve the foregoing objects, and in accordance with the purpose ofthe invention as broadly described herein, the present inventionprovides a computer-implemented method of processing market researchdata, including sales data concerning items sold during retail salestransactions of a retailer and advertising/promotion data concerning thesold items, the method comprising the steps of receiving the sales data;receiving the advertising/promotion data; enhancing the sales data byembedding elements of the advertising/promotion data in the sales data;performing association analysis on the enhanced sales data to generateassociation rules; and displaying and archiving the association rules.

The present invention will now be described with reference to thefollowing drawings, in which like reference numbers denote the sameelements throughout.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the functional components of a systemconstructed in accordance with the present invention;

FIG. 2 is a high level flowchart illustrating the overall process of thepresent invention;

FIG. 3 is a flowchart illustrating the aggregate property enhancementprocess block of FIG. 2;

FIG. 4 is a flowchart illustrating the advertising/promotionalenhancement block of FIG. 2;

FIG. 5 illustrates a three-level merchandising taxonomy;

FIG. 6 illustrates a taxonomy relation table linking the category levelto the SKU level of the taxonomy illustrated in FIG. 5;

FIG. 7 illustrates a taxonomy relation table linking the category levelto the department level for the taxonomy illustrated in FIG. 5;

FIG. 8 illustrates a three-level advertising taxonomy;

FIG. 9 illustrates a taxonomy relation table linking an item level to aflyer/page level of the advertising taxonomy illustrated in FIG. 8;

FIG. 10 illustrates a taxonomy relation table linking the flyer/pagelevel to the flyer level of the advertising taxonomy illustrated in FIG.8; and

FIG. 11 is a flowchart illustrating one example of the post-processingstep of FIG. 2.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 illustrates an overview of the functional components of a system100 for researching the sales effects of advertising using associationanalysis in accordance with the present invention. While the examplesgiven herein are directed to a standard retail environment, the presentinvention is not limited to such an application, and it is clear thatthe principles and methods of the present invention can find applicationin numerous other settings including electronic commerce (“E-Commerce”)over the Internet and any other application in which it is desirable toanalyze the effect of advertising or promotion on consumer behavior.

As used herein, the term “Retailer” refers to a person or organizationneeding analysis of data and conclusions derived from the data. Examplesof typical Retailers include the marketing department of a retail storeor E-Commerce organization, a buyer for a retail store, or a websitedesigner designing a website for an E-Commerce organization.

As used herein, the term “User” refers to a person, organization, orautomated device that uses the present invention in connection withinput from a Retailer to provide the desired data analysis andconclusions. Examples of typical users include a market analysisorganization, a retailer who possesses a system in accordance with thepresent invention, or a web designer for an E-Commerce organization thathas access to a system in accordance with the present invention.

As used herein, the term “Purchaser” refers to a customer of a Retailerwho purchases items from the Retailer. Examples of typical Purchasersinclude an individual shopper at a retail store or an individual makingpurchases over the Internet from an E-Commerce organization.

The system 100 includes Retailer information input/output devices 102,an analysis server 104, a database server 106 and data input devices108, 110. Retailer information input devices 102 provide means forinputting to the system information regarding the needs and desires of aparticular Retailer. The Retailer information can be manually input bythe User of the system via, for example, a standard keyboard; in thisscenario, the Retailer information is manually gathered by the User byinterview, questionnaire, or other known information-gatheringtechniques. In a preferred embodiment, the Retailer directly inputs theRetailer information by filling out an electronic questionnaire orinterview form available on a website of the User, thereby transmittingthe information to analysis server 104 via the Internet.

Analysis server 104 performs the association analysis on all data inputthereto. The general operation of the analysis server 104 is based onthe use of well known systems and tools such as IBM's Intelligent Minerdiscussed above. As described more fully below, however, it is theutilization of the data mining tools, the enhancement of the dataanalyzed by the data mining tools and the post-processing andinterpretation of the findings that represent some of the novel aspectsof the present invention.

Database server 106 stores selected point-of-sale (POS) data,advertising and promotion data, and product data, all of which isobtained from a variety of general data sources, including POS systems108, marketing department databases 110 and the like.

Each of the elements of the system can communicate with each other inany known manner, for example, over a network connection or via standardcabling. Analysis server 104 includes an interface 112, a server 114(e.g., an HTTP or Internet web server) and a controller 116. Interface112 allows the analysis server 104 and the other elements of the systemto communicate with each other in a known manner. Server 114 managescommunications between the input devices 102 and the database server 106in a known manner. Controller 116 controls the operation of server 114,including all communications between preparation engine 118, associationanalysis engine 120 and post-processing engine 122. In general,preparation engine 118, association analysis engine 120, andpost-processing engine 122 are known components found in most datamining systems. However, the preparation engine 118 and post-processingengine 122 are utilized in a novel manner to achieve the results of thepresent invention, as described in more detail below.

FIG. 2 is a high level flowchart illustrating the overall process of thepresent invention. At step 210 analysis and specification parameters areacquired. These parameters are input to the system via a Retailer inputdevice (e.g., one of the Retailer input devices 102 of FIG. 1) anddefine what it is that the Retailer is interested in knowing about. Thisinformation could include, for example, details regarding which POS datathe Retailer is interested in analyzing (store locations, specific timeperiods, product lines); which hierarchy to use (e.g., merchandiseand/or advertisement taxonomy, described below); minimum support andconfidence thresholds, and item constraints (e.g., which product item(s)to include or exclude from the analysis).

At step 220 the data required to perform the various analyses requestedby the Retailer in step 210 is collected and prepared. The collectionaspect of step 220 involves the gathering of selected data from thegeneral data sources, for example, the POS system databases 108 andmarketing department databases 110, by the database server 106. Thisassures that only necessary information is utilized and unnecessaryinformation is excluded.

The preparation aspect of step 220 involves the preparing of thecollected data for association analysis by assigning identificationnumbers for each of the events or items under scrutiny (addingidentification and transaction information), for example a particularmarket basket and the items in the market basket; inserting the data forthe revenue resulting from the sale of each item and the cost of eachitem (financial information); and adding details of any advertising thatmay have been done for each item. The exact data inserted will depend onthe analysis requested by the Retailer and may be very detailed or onlyvery general in nature. All of the data to be inserted is available fromthe POS system database 108 and the marketing department database 110.

In accordance with the present invention, the data is also enhanced, asdescribed further below, by (a) embedding the data with informationregarding advertisements and promotions; and/or (b) by identifying theaggregate characteristics of each market basket and embedding the marketbasket data with information regarding these aggregate properties. Thepreparation step allows the association analysis step at block 230 totake into account and process the parameters requested by the Retailerand the enhancement of the data enables the present invention to providethe Retailer with additional relevant information that is not availableusing prior-art market basket analysis methods.

At step 230, an association analysis is performed in a known mannerusing standard association analysis algorithms to process the data thathas been collected and enhanced in step 220. In a well-known manner, theassociation analysis step generates association rules for the data.However, as discussed below, the rules are much more useful because ofthe enhancements introduced in step 220. Thus, the post-processing stepsperformed on the enhanced data at step 240 (described in more detailbelow with respect to FIG. 11) involve processing of the data based onparameters not considered by prior art systems, and thereby yieldsignificantly better information for presentation and archiving at step250.

Finally, at step 260 a determination is made as to whether additionalanalysis is required or desired. If not, the process ends. However, ifmore analysis is required, then the process returns to step 210 andbegins again. There are numerous situations when additional analysismight be required. For example, it may be desired by the Retailer to runan analysis before a promotion is implemented, during the promotion, andafter the promotion has ended so that changes in purchasing behavior canbe identified and evaluated; to compare different stores or groups ofstores (e.g., on a regional level); or to run an analysis of products atboth a category level and a department level.

FIG. 3 is a flowchart illustrating what is referred to herein as the“aggregate property” enhancement process of block 220 of FIG. 2 Theaggregate-property enhancement process illustrated with respect to FIG.3 enables the discovery of patterns that characterize or discriminatemarket baskets having particular overall properties, i.e., the marketbasket dynamics. Aggregate properties of a market basket would be, forexample, a market basket that has an overall negative gross margin or amarket basket that has an overall “high” gross margin. As shown in FIG.3, a logical grouping of data (e.g., all the items contained in a marketbasket) are identified as possessing, as a whole, one or more specifiedproperties.

Referring now to FIG. 3, at step 310, data pertaining to a particularmarket basket (e.g., the market basket of Purchaser “David” on August30th) is input and, at step 312, a determination is made as to whetheror not a specified property about the market basket is true. Thus, forexample, if the specified property being analyzed is the gross margin ofthe entire basket, and the Retailer has determined that a “high” grossmargin market basket would be any basket that has an overall grossmargin exceeding $50, at step 312, if the market basket input at step310 has a gross margin that is $50 or less, the process proceeds to step316 and the ordinary “non-enhanced” market basket information is writtento the analysis file, and at step 318, a determination is made as towhether or not there is another market basket to be analyzed. If thereare no additional market baskets to be analyzed, the process ends; ifthere is another market basket to be analyzed, the process proceeds backto step 310 and the process is continues until all baskets have beenanalyzed.

If, at step 312, it is determined that the property “gross margin” ofthe market basket “David” is high, i.e., that it exceeds $50, then atstep 314 a designation indicating the existence of this property “added”to the basket to identify this property as a characteristic of thebasket. These designations are referred to as “imaginary items” andenhance the market basket data by categorizing the market basket aspossessing the designated property. As an example, at step 314, animaginary item “HM” is “added” to the basket (i.e., the market basketdata is modified to include a designation “HM”) to indicate that thebasket is a high margin basket. The addition of the imaginary item cancomprise a simple coding process, wherein an identifier is added to thedata for the market basket “David” which, in this example, identifiesthe basket as a high margin basket. Each imaginary item type requestedby the retailer must have a different code so that they can bedistinguished from each other, and it is also desirable, for efficiency,to make the imaginary items easily distinguishable from real items.

The process then proceeds to step 316 where the market basketinformation, including the now enhanced basket information, is writtento the analysis file. The process may be repeated for as many propertiesas desired so that a basket may possess plural imaginary itemsidentifying plural aggregate properties of the basket.

As noted above, in addition to characterizing market baskets, thepresent invention also enables analysis of the effects ofadvertising/promotion on the sale of products. During theadvertising/promotional enhancement process all items in the databaseare classified using a standard merchandise taxonomy; at the same time,however, the items are also classified using an advertisement taxonomy,which enables association analysis to produce patterns that involveelements of the advertising media used. These taxonomies are describedin more detail below with respect to FIGS. 5-10.

FIG. 4 is a flowchart illustrating what is referred to herein as the“advertising/promotional enhancement.” process of block 220 of FIG. 2.This process enables association analysis to take into account theadvertising status for items at the time of sale. Referring now to FIG.4, at step 410 data pertaining to a next (or first) item in a sequenceof transactions is obtained. This data comprises information regardingan individual purchase of an item in a market basket. At step 412, adetermination is made as to whether or not the item was being advertisedand/or promoted when it was purchased (based on data gathered duringstep 220 of FIG. 2). If it is determined that the item was notadvertised or promoted, at step 414 information is added to the datacorresponding to the item to create an “enhanced item” that identifiesthe item as a non-advertised item, and the process proceeds back to step410.

If, at step 412, it is determined that the item was advertised orpromoted when it was purchased, then at step 416 the item is designatedas being an advertised item, details of the advertisement are added toenhance the data pertaining to the item. These details may includelow-level elements of the advertising media such as the particularquadrant of a page on which an advertisement appeared, high-levelelements of the advertising media such as the year in which anadvertisement appeared, or mid-level details falling between the low andhigh-level elements. The advertising elements are used in an advertisingtaxonomy, described below with respect to FIGS. 8-10, to allow theadvertising information to be associated with other pertinentinformation.

Next, the process proceeds to step 418 to determine if the same productwas advertised in another advertisement when it was purchased. This isdone because many times a single product is advertised in multiplelocations, by different methods, etc. If it is determined that there wasanother advertisement running for the same product when it waspurchased, then it is flagged as such and the process proceeds back tostep 416 so that the item may be designated as being advertised morethan once and the details regarding the additional advertising/promotionprograms can be added. This can be accomplished by, for example,introducing a replica of the enhanced item data into the market basket,changing only the information specific to the additionaladvertisement(s). This is repeated until it is determined that there areno more additional ads/promotions for the item, at which point theprocess proceeds back to step 410 and continues until alltransactions/items have been processed.

In order to be able to use the enhanced data in association analysis,each item in the database is classified using a standard merchandisetaxonomy, as well as an advertisement taxonomy. This classificationprocess is described with reference to FIGS. 5-10. Referring now to FIG.5, a simple 3-level merchandising taxonomy is described. In practicalapplication, the merchandise taxonomy could consist of many more levels,depending on the desired level of “resolution” of the data analysis andthe organization of the business of the Retailer.

The idea behind any taxonomy classification is to establish linksbetween various levels of classification so that “children” within thetaxonomy can be linked to items of common “ancestry.” For example, themerchandise taxonomy of FIG. 5 shows a three-level taxonomy populatedwith typical, basic sales data: a Department level; a Category level;and an Item or SKU (e.g., any product identification code used in theretail industry) level. Taking beverages as an example of a particulartype of merchandise, at the Department level, the descriptions might be“alcoholic beverages,” “non-alcoholic beverages”; at the Category level,the descriptions might be “beer,” “wine,” “cordials”; and at the Itemlevel, the descriptions might be “Heineken six-pack 12 Oz. Bottles,”“Corona six-pack 12 Oz. Cans,” “Gallo Merlot 750 ml.,” “Kendall JacksonPinot Noir 750 ml.”

To be able to use this information, relationships have to be establishedto link this information from the lowest level to the highest level. Forexample, FIG. 6 illustrates a taxonomy relation table (exemplaryinformation illustrated only) linking the Category level to the Itemlevel for the previously described example. As can be seen in FIG. 6,Item's “Heineken six-pack 12 Oz. Bottles” and “Corona six-pack 12 Oz.Cans” are each associated or linked to the category “Beer”; and Item's“Gallo Merlot” and “Kendall Jackson Pinot Noir” are associated with thecategory “Wine.”

FIG. 7 illustrates a taxonomy relation table linking the Category levelto the Department level for the same example. As can be seen in FIG. 7,each of the categories “Beer” and “Wine” are separately associated withthe Department “Alcoholic Beverages.” Creation of these taxonomies iswhat enables association analysis to identify patterns that involveitems at various levels, and this is especially important whenlow-level, less-frequently occurring items are involved. Such items donot allow patterns to be easily established on their own; with thetaxonomies, however, these low level items may be considered at a higherlevel where patterns may be more easily established (e.g., if “GalloMerlot” is infrequently purchased, advertising for the category “Wines”may be analyzed instead, since the taxonomy establishes the link to boththe low-level and higher-level categories.

A novel aspect of the present invention is the use of associationanalysis to associate elements of the advertising media at variouslevels with each other, as well as with items in the merchandisetaxonomy. Obviously this “cross-taxonomy association” is not limited tothese two taxonomies and it is understood that other taxonomies could beassociated with each other as well.

FIG. 8 illustrates a simple 3-level advertising taxonomy. This taxonomyalso comprises three levels populated with advertising/promotion data: aFlyer level; a Flyer/Page level; and an Item level. By using a level(Item level) that is also used in the merchandise taxonomy, a link isestablished between the two taxonomies so that correlations between thetwo taxonomies can be made. As with the merchandise taxonomy describedabove, the addition of more levels in the taxonomy will increase theresolution of the analysis. Continuing with the same example, at theFlyer level, the description might be “September 23rd PhiladelphiaInquirer Flyers”; “September 30th Philadelphia Inquirer Flyers”; . . . ,December 22nd Philadelphia Inquirer Flyers”, etc.; for the Flyers/Pagelevel, the descriptions might be “September 23rd Philadelphia InquirerFlyer, Page 2”; December 22nd Burlington County Times Flyer, back page”;and for the Item level, the descriptions would be the same as for themerchandise taxonomy.

FIG. 9 illustrates a taxonomy relation table linking an Item level to a“Flyer/Page” level of an advertising piece according to this example.The “Flyer/Page” level refers to a particular page of a particularadvertising flyer on a specific date. Referring to FIG. 9, it can beseen that the Item “Heineken six-pack 12 Oz. Bottles” is associated withFlyer/Page “Philadelphia Inquirer, September 23rd/Front Page”, whichmeans that an advertisement for the Heineken Item of the exampleappeared on the front page of a flyer that was included in the September23rd edition of the Philadelphia Inquirer. Similarly, the Corona Item ofthe example is associated with the same advertising page and date(“Philadelphia Inquirer, September 23rd/Front Page”); the Gallo MerlotItem is associated with the back page of a December 22nd flyer includedwith the Burlington County Times (“Burlington County Times/December22nd/Back Page”); and the Kendall Jackson Item is associated with themiddle insert of the December 22nd Burlington County Times flyer(“Burlington County Times/December 22nd/Middle Insert”). Note that theHeineken Item was also advertised on the back page of the September 23rdPhiladelphia Inquirer.

Likewise, FIG. 10 illustrates a taxonomy relation table linking the“Flyer/Page” level of the taxonomy to the “Flyer” level (e.g., The frontpage of the September 23rd Philadelphia Inquirer flyer is associatedwith the September 23rd Philadelphia Inquirer flyer). Through thissimple example, it can be seen that the advertising for a particularItem can be associated with a particular page or pages in anadvertisement, and/or a particular advertising date for a particularpublication.

The taxonomy relations that are illustrated in FIGS. 5-10 set up the useof these taxonomies in performing the association analysis. Theserelationships are utilized, as described above, to enhance the marketbasket data prior to subjecting the data to association analysis. In aknown manner, the Items in the merchandise taxonomy can be linked toadvertisements (if any) in the advertising taxonomy.

FIG. 11 is a flowchart illustrating one example of post-processing step240 of FIG. 2 and, in particular, a post-processing step for identifyingpatterns that characterize or discriminate market baskets withparticular aggregate properties. As previously discussed, theassociation analysis step performed at step 230 of FIG. 2 generates aseries of rules which characterize each market basket, and many of theserules may have been enhanced by use of imaginary items, enhanced items,and/or taxonomies during the preparation of the data for processing.During the post-processing step, the analyzed, enhanced data is used todevelop conclusions about the data.

Referring to FIG. 11, at step 1110, a determination is made as towhether or not a rule has been generated by the association analysisstep for the item of interest, for example, for imaginary item HM. If norule is found, this indicates that the analysis is complete and theprocess terminates at that point. However, if at step 1110 it isdetermined that the next rule to be processed involves the item ofinterest, then at step 1114, a determination is made as to whether ornot the item of interest is part of the premise (e.g., before the arrow)of the rule or the consequent (e.g., after the arrow) of the rule. Forexample, if the item of interest is imaginary item HM, and if item HM ispart of the premise of the rule (e.g., HM→A+B, meaning “whenever a highmargin basket occurs, it tends to include both A and B”), the processproceeds to step 1120 and the lift value, which was calculated duringthe association analysis step, is analyzed. If, for example, it isdetermined that the lift is much greater or much less than 1, then it isconsidered an “interesting” rule (i.e., of interest to the Retailer) andthe process proceeds to step 1122 where the rule is deemed tocharacterize market baskets that have the property HM as being highmargin baskets (and allows the inference that there is a good likelihoodthat the basket contains A and B) and then the process proceeds back tostep 1110. If the lift is found to be at or near 1, the rule isconsidered “uninteresting” and it is not designated as having anyparticular property, and the process proceeds back to step 1110.

If, on the other hand, at step 1114 it is determined that item HM is notpart of the premise (i.e., that item HM is part of the consequent, e.g.,A+B→HM, meaning “whenever A and B occur together in a market basket,then it is a high margin basket”), then at step 1116, a determination ismade as to whether the lift value is much greater or much less than 1.If the lift value is much greater or much less than 1, then at step 1118the rule is deemed to discriminate market baskets that have the propertyHM from other baskets that do not have this property (and allows theinference that since the basket contains items A and B, there is a highlikelihood that the basket is a high margin basket) and then the processproceeds back to step 1110. If, at step 1116, there is a determinationmade that the lift value is at or nearly 1, then the process justreverts back to step 1110.

Although the present invention is described in connection with marketingresearch, it is understood that the techniques and methods describedherein can be applied to any type of research in which it is desired tocharacterize data groupings and/or analyze the effects of a particularparameter (e.g., other than advertising). Further, it is understood thatthe properties of the market baskets can include information other thanfinancial information, for example, a market basket can be characterizedas containing advertised and/or non-advertised items and thisinformation can be used by researchers as well.

Although the present invention has been described with respect to aspecific preferred embodiment thereof, various changes and modificationsmay be suggested to one skilled in the art and it is intended that thepresent invention encompass such changes and modifications as fallwithin the scope of the appended claims.

1. A computer-implemented method of processing market research dataincluding sales data concerning items sold during retail salestransactions of a retailer and advertising/promotion data concerningsaid sold items, said method comprising the steps of: receiving saidsales data; receiving said advertising/promotion data; enhancing saidsales data by embedding elements of said advertising/promotion data insaid sales data; performing association analysis on said enhanced salesdata to generate association rules and frequent itemsets; and displayingand archiving said association rules and frequent itemsets.
 2. Themethod as set forth in claim 1, further comprising the step of:processing said association rules and frequent itemsets to developconclusions about said marketing research data.
 3. The method as setforth in claim 2, wherein said sales data comprises merchandiseinformation, said merchandise information including: identificationinformation identifying each sold item; transactional informationcorresponding to each sold item; and financial information correspondingto each sold item; and wherein said merchandise information is input toa merchandise taxonomy to establish logical links between saididentification information, said transactional information, and saidfinancial information so that said merchandise information can beutilized for market basket analysis.
 4. The method as set forth in claim3, wherein said advertising/promotional data comprises informationidentifying the advertising status of each item, and wherein saidadvertising status information is embedded in said merchandisinginformation to establish a logical link between said merchandiseinformation and said advertising/promotional data to create saidenhanced sales data.
 5. The method as set forth in claim 3, wherein saidadvertising/promotional data comprises: information identifying itemsthat have been advertised; and for said advertised items, informationidentifying how the item was advertised, the date the item wasadvertised, and where the item was advertised; and wherein saidadvertising information is embedded in said merchandising information toestablish a logical link between said merchandise information and saidadvertising/promotional data, thereby creating said enhanced sales data.6. The method as set forth in claim 5, wherein said informationidentifying how each advertised item was advertised includes anidentification of the advertising medium used and the placement of theadvertisement in the advertising medium.
 7. The method as set forth inclaim 6, wherein said enhancing step comprises the steps of: classifyingsaid advertising/promotion information in an advertising/promotiontaxonomy; and creating said enhanced data by embedding said informationin said advertising/promotion taxonomy into said merchandise taxonomy.8. A computer-implemented method of analyzing data comprising pluraldata elements and presenting the analyzed data for use by a dataanalyst, comprising the steps of: acquiring analysis parametersaccording to which the data is to be analyzed; collecting data elementscorresponding to said acquired analysis parameters; enhancing saidcollected data elements in accordance with said analysis parameters;performing association analysis on said enhanced data elements togenerate enhanced association rules and frequent itemsets; anddisplaying and archiving said enhanced association rules and frequentitemsets.
 9. The method as set forth in claim 8, further comprising thestep of: processing said enhanced association rules and frequentitemsets to develop conclusions about said analyzed data.
 10. The methodas set forth in claim 9, wherein said collected data elements includemerchandise information and advertising/promotion information pertainingto items sold by a retailer, and wherein said enhancement step comprisesthe steps of: classifying said merchandise information in a merchandisetaxonomy; classifying said advertising/promotion information in anadvertising/promotion taxonomy; and creating enhanced data elementscorresponding to said items sold by said retailer, said enhanced dataelements providing a correlation between each item and anyadvertising/promotion conducted in connection with each item.
 11. Acomputer program product recorded on computer readable medium foranalyzing data comprising plural data elements and presenting theanalyzed data for use by a data analyst, comprising: computer readablemeans for acquiring analysis parameters according to which the data isto be analyzed; computer readable means for collecting data elementscorresponding to said acquired analysis parameters; computer readablemeans for enhancing said collected data elements in accordance with saidanalysis parameters; computer readable means for performing associationanalysis on said enhanced data elements to generate enhanced associationrules and frequent itemsets; and computer readable means for displayingand archiving said enhanced association rules and frequent itemsets. 12.The computer program product as set forth in claim 11, furthercomprising: computer readable means for processing said enhancedassociation rules and frequent itemsets to develop conclusions aboutsaid analyzed data.
 13. The computer program product as set forth inclaim 12, wherein said collected data elements include merchandiseinformation and advertising/promotion information pertaining to itemssold by a retailer, and wherein said computer readable means forenhancing includes: computer readable means for classifying saidmerchandise information in a merchandise taxonomy; computer readablemeans for classifying said advertising/promotion information in anadvertising/promotion taxonomy; and computer readable means for creatingenhanced data elements corresponding to said items sold by saidretailer, said enhanced data elements providing a correlation betweeneach item and any advertising/promotion conducted in connection witheach item.
 14. A system of analyzing data comprising plural dataelements and presenting the analyzed data for use by a data analyst,comprising: means for acquiring analysis parameters according to whichthe data is to be analyzed; means for collecting data elementscorresponding to said acquired analysis parameters; means for enhancingsaid collected data elements in accordance with said analysisparameters; means for performing association analysis on said enhanceddata elements to generate enhanced association rules and frequentitemsets; and means for displaying and archiving said enhancedassociation rules and frequent itemsets.
 15. The system as set forth inclaim 14, further comprising: means for processing said enhancedassociation rules and frequent itemsets to develop conclusions aboutsaid analyzed data.
 16. The system as set forth in claim 15, whereinsaid collected data elements include merchandise information andadvertising/promotion information pertaining to items sold by aretailer, and wherein said means for enhancing comprises: means forclassifying said merchandise information in a merchandise taxonomy;means for classifying said advertising/promotion information in anadvertising/promotion taxonomy; and means for creating enhanced dataelements corresponding to said items sold by said retailer, saidenhanced data elements providing a correlation between each item and anyadvertising/promotion conducted in connection with each item.